Siamese-network based Tracker.
To handle scale variation of the target in the cycle, our ULAST is built upon Siamese-network based region proposal network [1]}.
Suppose we need to use a template patch to find the target object in a search region. The tracker first use a ResNet50 with shared parameter to extract the feature from the template and search region, and we denote the extracted feature from the template and search region patches as \(T\)  and \(S\) , respectively. Then, a region proposal network is learnt to generate the bounding box and the corresponding class. The network is optimized with classification loss and regression loss:
\(L^{l} = \lambda _{1} L_{cls}^{l} + \lambda _{2} L_{reg}^{l},\) 
